Recently, a driver model is proposed to be used for assisting a vehicle control. For example, JP2009-237937A discloses a driver model processor which uses driver models particularly regarding driving operations. The driver models include an individual driver model created for a particular vehicle and an optimal driver model created based on data of a large number of vehicles by a driver model server disposed outside the vehicle. In this processor, when the individual driver model is different from the optimal driver model, a vehicle driver is given advice based on this difference.
Further, learning systems for vehicles are proposed. For example, JP2015-135552A discloses a learning system that transmits data used for image recognition processing from a vehicle to a learning server, and the learning server performs learning processing by using this data. Thus, update data of a parameter used for the image recognition processing is generated and the parameter is updated with the update data in the vehicle.
In view of securing a suitable vehicle control, it is preferable that vehicle control processing is consecutively updatable based on learning in an external machine learning system as described in JP2015-135552A. It is particularly preferable if the vehicle control processing is updatable so as to match with driving characteristics of an individual driver. However, since an emotional state of the driver is not constant, fixed vehicle control processing is not always suitable for the driver if his/her emotional state changes. However, for example, if an unexpected extreme operation is repeated, because of the vehicle control processing being updated accordingly, negative influences may occur, such as a performance degradation of the vehicle in an early stage and an increase of driver fatigue.